name: Resnets on ImageNet SINGA version: 1.1 SINGA commit: 45ec92d8ffc1fa1385a9307fdf07e21da939ee2f parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz license: Apache V2, https://github.com/facebook/fb.resnet.torch/blob/master/LICENSE

Image Classification using Residual Networks

In this example, we convert Residual Networks trained on Torch to SINGA for image classification.

Instructions

• Download one parameter checkpoint file (see below) and the synset word file of ImageNet into this folder, e.g.,

    $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz
    $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt
    $ tar xvf resnet-18.tar.gz
  

• Usage

    $ python serve.py -h
  

• Example

    # use cpu
    $ python serve.py --use_cpu --parameter_file resnet-18.pickle --model resnet --depth 18 &
    # use gpu
    $ python serve.py --parameter_file resnet-18.pickle --model resnet --depth 18 &
  

  The parameter files for the following model and depth configuration pairs are provided:

  • resnet (original resnet), 18|34|101|152
  • addbn (resnet with a batch normalization layer after the addition), 50
  • wrn (wide resnet), 50
  • preact (resnet with pre-activation) 200

• Submit images for classification

    $ curl -i -F image=@image1.jpg http://localhost:9999/api
    $ curl -i -F image=@image2.jpg http://localhost:9999/api
    $ curl -i -F image=@image3.jpg http://localhost:9999/api
  

image1.jpg, image2.jpg and image3.jpg should be downloaded before executing the above commands.

Details

The parameter files were extracted from the original torch files via the convert.py program.

Usage:

$ python convert.py -h